Interpretable Distillation Reveals that Deep-learning-based Splicing Models Suffer from Pervasive Confounders and Blind Spots

Liu, S.; Zhang, W.; Regev, O.

doi:10.1101/2025.11.28.691252

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bioRxiv · 10.1101/2025.11.28.691252

Interpretable Distillation Reveals that Deep-learning-based Splicing Models Suffer from Pervasive Confounders and Blind Spots

Liu, S.·Zhang, W.·Regev, O.

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Abstract

Despite their growing popularity, genomic deep-learning-based models function largely as black boxes, raising concerns about their trustworthiness. Here we develop a framework to explain model prediction logic using interpretable distillation. Applying our framework, we find that RNA splicing prediction models suffer from pervasive confounders and blind spots, leading to poor performance on non-reference sequences. Our findings illuminate fundamental limitations of training models on genomic sequences and suggest ways to overcome them.

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Liu, S., Zhang, W., Regev, O.. 2025-12-01. Interpretable Distillation Reveals that Deep-learning-based Splicing Models Suffer from Pervasive Confounders and Blind Spots. https://doi.org/10.1101/2025.11.28.691252

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